Adaptabilidade e estabilidade produtiva de genótipos de feijão-caupi semieretos no Nordeste do Brasil via REML/BLUP

rca Revista Ciência Agronômica Rev. Ciênc. Agron. 0045-6888 1806-6690 Universidade Federal do Ceará RESUMO O feijão-caupi é cultivado em diferentes condições edafoclimáticas da região Nordeste do Brasil. Devido à existência da interação entre genótipos e ambientes, torna-se necessário selecionar e desenvolver cultivares para essa região com alta produtividade, estabilidade e adaptabilidade. Este trabalho teve como objetivo selecionar linhagens de feijão-caupi de porte semiereto, simultaneamente, para alta produtividade, adaptabilidade e estabilidade genotípica na região Nordeste do Brasil, via procedimento REML/BLUP. Foram avaliados 20 genótipos de porte semiereto, sendo 15 linhagens e cinco cultivares, em 37 ambientes da Região Nordeste, no período de 2013 a 2015. Os experimentos foram conduzidos em condições de sequeiro, em delineamento de blocos completos casualizados, com quatro repetições. A adaptabilidade e estabilidade genotípica foram avaliadas via procedimento REML/BLUP. A variância ambiental foi o maior componente da variância fenotípica e a interação genótipos x ambientes foi do tipo complexa, com a produtividade de grãos variando de 277 kg ha-1 (Serra Talhada-PE, 2015) a 2.845 kg ha-1(São Raimundo das Mangabeiras-MA, 2013) e média geral de 1.342 kg ha-1. De acordo com as estimativas de média harmônica de desempenho relativo de valores genotípicos (MHPRVG), as linhagens MNC04-795F-153 e MNC04-795F-159 reúnem simultaneamente alta produtividade, estabilidade e adaptabilidade genotípica, podendo ser indicadas com maior probabilidade de sucesso para o cultivo nos ambientes de avaliação da região Nordeste do Brasil. INTRODUCTION Cowpea (Vigna unguiculata (L.) Walp.) is an important legume in the Northeast region of Brazil, and a source of energy and protein. The cultivation of this species generates employment and income for the population of this region. Cowpea is the second most consumed type of bean in Brazil, and its cultivation is concentrated in the North, Northeast, and Center-West regions. There are no official statistics on cowpea production in Brazil; thus, based on the estimates of CONAB (2017), the Northeast region presented in 2016 an area of 1,029,600 ha with a production of 196,100 Mg. This represents 82.56% and 54.00% of the area and production of this legume in Brazil, respectively. The largest national producers are the states of Mato Grosso, Ceará, and Maranhão. The total area for cowpea production in Brazil is 1,247,100 ha and its production is 362,500 Mg, which represents 14.43% of all beans produced in Brazil. Considering that cowpea is cultivated in a wide range of environments in Brazil, but the occurrence of genotype × environment interaction (G×E) may hinder the selection of superior genotypes and the recommendation of cultivars (CARVALHO et al., 2016; OLIVEIRA; FONTES; ROCHA, 2015; ROCHA et al., 2012). Thus, in the final stages of a breeding program, elite lines are tested in various environments; this allows an investigation of the magnitude of the G×E, adaptability, and yield stability, which subsidize the recommendation of cultivars for broad or specific conditions of adaptation. In the last 60 years, several methodologies to study the adaptability and stability of genotypes in multiple environments were developed to help the breeder to select the most stable and suitable genotypes for crops in different environments (CARVALHO et al., 2016). The most commonly methodologies used in cowpea are the AMMI (BARROS et al., 2013; DDAMULIRA et al., 2015; SANTOS et al., 2015), GGE Biplot (OKORONKWO; NWOFIA, 2016; OLAYIWOLA; SOREMI; OKELEYE, 2015; SANTOS et al., 2016), Bayesian approache (BARROSO et al., 2016; TEODORO et al., 2015), and artificial neural networks (TEODORO et al., 2015). The REML/BLUP procedure has been one of the most used techniques in studies on adaptability and genotypic stability; it is based on mixed models. This procedure estimates the components of variance by the Restricted Maximum Likelihood (REML) and the prediction of genetic values by the Best Linear Unbiased Prediction (BLUP) (CARIAS et al., 2014). It was originally recommended for studies on quantitative genetics and selection of perennial plants (RESENDE, 2007a,b); however, it has also been used in annual species such as rice (BORGES et al., 2010), common bean (CHIORATO et al., 2008; PEREIRA et al., 2016), soybean ( TESSELE et al.,2016), and wheat (SILVA et al., 2011). Adaptability and stability studies in cowpea using the REML/BLUP procedure have increased (SANTOS et al., 2016; TORRES et al., 2015, 2016). This is because it allows simultaneous selection for yield, adaptability, and stability in the context of mixed models through the use of the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV) method, proposed by Resende (2004). The objective of this work was to select semi-erect cowpea lines simultaneously for high yield, adaptability, and genotypic stability in the Northeast region of Brazil by the REML/BLUP procedure. MATERIAL AND METHODS Twenty cowpea genotypes of semi-erect plant were evaluated-15 lines and five cultivars-from the Embrapa Meio-Norte Cowpea Breeding Program (Table 1). These lines are part of the value-of-cultivation and use (VCU) trials, which are required for registering new cultivars by the National Register of Cultivars (RNC) of the Ministry of Agriculture, Livestock, and Food Supply (MAPA). These genotypes were selected in intermediate trials, which precede the VCU trials. Table 1 Semi-erect cowpea genotypes evaluated in the Northeast region of Brazil, from 2013 to 2015, their genealogy and commercial subclass (CS) GC Genotype Genealogy CS G21 MNC04-762F-3 TE96-282-22G × (TE96-282-22G × Vita 7) BL G22 MNC04-762F-9 TE96-282-22G × (TE96-282-22G × Vita 7) BL G23 MNC04-769F-30 CE-315 × TE97-304G-12 ML G24 MNC04-769F-48 CE-315 × TE97-304G-12 ML G25 MNC04-792F-146 MNC00-553D-8-1-2-3 × TV×5058-09C ML G26 MNC04-769F-62 CE-315 × TE97-304G-12 ML G27 MNC04-782F-104 (TE97-309G-24 × TE96-406-2E-28-2) × TE97-309G-24 SV G28 MNC04-792F-143 MNC00-553D-8-1-2-3 × TV×5058-09C ML G29 MNC04-792F-144 MNC00-553D-8-1-2-3 × TV×5058-09C SV G30 MNC04-792F-148 MNC00-553D-8-1-2-3 × TV×5058-09C ML G31 MNC04-795F-153 MNC99-518G-2 × IT92KD-279-3 ML G32 MNC04-795F-154 MNC99-518G-2 × IT92KD-279-3 SV G33 MNC04-795F-155 MNC99-518G-2 × IT92KD-279-3 ML G34 MNC04-795F-159 MNC99-518G-2 × IT92KD-279-3 ML G35 MNC04-795F-168 MNC99-518G-2 × IT92KD-279-3 BR G36 BRS Guariba IT85F-2687 × TE87-98-8G BL G37 BRS Tumucumaque TE96-282-22G × IT87D-611-3 BL G38 BRS Novaera TE97-404-1F × TE97-404-3F BR G39 BRS Itaim MNC01-625E-10-1-2-5 × MNC99-544D-101-2-2 FR G40 BRS Cauamé TE93-210-13F × TE96-282-22G BL GC = Genotype code; ML = Mulato; FR = Fradinho; SV = Sempre-verde; BL = Branco Liso; BR = Branco rugoso The genotypes were evaluated for grain yield (kg ha-1) in 22 locations in the Northeast region of Brazil (Table 2) under rainfed conditions from 2013 to 2015. Some VCU trials were conducted in 1, 2, or 3 crop seasons totaling 37 environments. Each environment was represented by the location initial letters and year of the crop season: APOD13, ARAP13, BARR13, CARI13, FREI13, ITAP13, MATA13, REDE13, SRMA13, UMBA13, URUC13, ARAR14, CARI14, BARR14, FEIR14, FREI14, GOIA14, IPAN14, ITAP14, MATA14, SERT14, SUMBA14, ARAR15, AROE15, CAMP15, FEIR15, FREI15, IPAN15, GOIA15, LAGS15, NSDO15, PACA15, SAOJ15, SERT15, SRMA15, UMBA15, and URUC15. Table 2 Altitude, geographic coordinate, biome, ecosystem, and climate of the locations where the value-of-cultivation and use trials of semi-erect cowpea were conducted in the Northeast region of Brazil, from 2013 to 2015 Location Altitude Latitude Longitude Biome Ecosystem Climate Arapiraca-AL 264 m 09º45'07'' S 36º39'39'' W Caatinga Agreste TSu Feira de Santana-BA 234 m 12º16'01'' S 38º58'01'' W Caatinga Sertão BSh Barreira-CE 123 m 04º17'13'' S 38º38'34'' W Caatinga Sertão BSh Itapipoca-CE 109 m 03º29'38'' S 39º34'44'' W Caatinga Sertão TAs Redenção-CE 88 m 04º13'30'' S 38º43'46'' W Caatinga Sertão TQSu Pacajus-CE 60 m 04º10'22'' S 38º27'39'' W Caatinga Sertão BSh Mata Roma-MA 73 m 03º37'30'' S 43º06'39'' W Cerrado Meio-Norte TAw São R. Mangabeiras-MA 225 m 07º01'19'' S 45º26'51'' W Cerrado Meio-Norte TAw Lagoa Seca-PB 634 m 07º10'15'' S 35º51'14'' W Caatinga Agreste TAs Aroeiras-PB 363 m 07º32'42'' S 35º42'28'' W Caatinga Agreste TAs Araripina-PE 622 m 07º34'33'' S 40º29'52'' W Caatinga Sertão BSh Serra Talhada-PE 444 m 07º59'09'' S 38º17'45'' W Caatinga Sertão BSh Goiana-PE 444 m 07º33'39'' S 35º00'10'' W Caatinga Zona da Mata TAs São João do Piauí-PI 222 m 08º21'28'' S 42º14'49'' W Caatinga Sertão BSh Campo Grande do Piauí-PI 443 m 07º07'55'' S 41º02'09'' W Caatinga Sertão BSh Uruçuí-PI 167 m 07º13'44'' S 44º33'41'' W Cerrado Meio-Norte TAw Apodi-RN 67 m 05º39'50'' S 37º47'56'' W Caatinga Sertão BSh Ipanguaçu-RN 16 m 05º29'52'' S 36º51'18'' W Caatinga Sertão BSh Carira-SE 351 m 10º21'29'' S 37º42'03'' W Caatinga Sertão BSh Frei Paulo-SE 272 m 10º32'56'' S 37º32'02'' W Caatinga Sertão BSh Umbaúba-SE 130 m 11º22'58'' S 37º39'28'' W Caatinga Sertão TAs Nossa S. das Dores-SE 204 m 10º29'30'' S 37º11'36'' W Caatinga Sertão TAs TSu = Tropical sub-humid; TQSu = Tropical hot sub-humid; BSh = Semi-arid mild; TAs = Tropical with dry summer season; Taw = Tropical with dry winter season; Ts = Tropical dry The planting season varied according to the rainy season in the states, which occurred in January to March, except in Alagoas and Sergipe, which occurred in June. All the trials were conducted in a complete randomized block design with 20 treatments and four replications. The treatments were represented by a plot of 2.0 m x 5.0 m with four 5.0 m rows spaced 0.50 m apart, with 0.25 m between plants. The evaluation area was represented by the two central rows. Four seeds were sown per hole, and 20 days after sowing the plants were thinned, leaving two plants per hole. Cultural practices were carried out for the crop as recommended. Individual analysis of variance for environments and joint analysis of variance for all environments were performed. The joint analysis of variance considered the effect of genotypes as fixed, and the effect of environments as random. The statistical model used followed the equation: (1) Y ijk = μ + g i + e j + ge ij + β k J + ε ijk where in: Yijk is the observed value of the genotype i in the environment j and block k ; µ is the overall mean of the trait; gi is the effect of the genotype i ; ej is the effect of the environment j ; geij is the effect of the interaction of the genotype i with the environment j ; βk(J) is the effect of the block k within the environment j ; and εijk is the experimental error associated with the plot ijk. The mixed models approach through Restricted Maximum Likelihood (REML) and Best Linear Unbiased Prediction (BLUP) multivariate, i.e., REML/BLUP procedure, (RESENDE, 2007b) was used for the analysis of adaptability and stability. This procedure is a method for ordering the genotype simultaneously regarding their genetic values (yield) and stability; it represents the BLUP procedure under the harmonic mean of the data. The lower the standard deviation of the genotypic behavior in the environments, the greater the harmonic mean of genotypic values (HMGV) in the environments. Thus, selection by the highest HMGV implies both selection for yield and stability (RESENDE, 2007b). In the context of the mixed models, a simple and efficient measure is the relative performance of genotypic values (RPGV) in the environments, i.e., the adaptability of genetic values. The quantity RPGV*OM refers to the relative performance genotypic value multiplied by the overall mean of all environments, providing the average genotypic value, capitalizing the adaptability (RESENDE, 2007b). Simultaneous selection for yield adaptability and stability in the context of mixed models can be performed by the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV), proposed by Resende (2004). The quantity HMRPGV*OM refers to the HMRPGV multiplied by the overall mean of all environments, which provides the mean genotypic value penalized by instability and capitalized by adaptability. The following statistical model was used for the randomized block designs with one observation per plot and several environments: (2) Y = Xb + Zg + Wga + e where in: y, b, g, ga, and . are the data vectors of fixed effects (mean of blocks through environments), of genotypic effects of the genotype (random), of effects of the genotype × environment interaction (G×E) (random), and of random errors, respectively; X, Z and . are the incidence matrices for b, g, and ga, respectively. The Harmonic Mean of Genotypic Values (HMGV) was used for the evaluation of stability; the RPGV was used for the simultaneous evaluation of yield and adaptability; and the HMRPGV was used for the simultaneous evaluation of yield, adaptability, and stability. These evaluation were carried out using the following expressions: (3) HMGV i = a / ∑ i = 1 a 1 / Vg j (4) RPGV i = 1 / a ∑ Vg j / M j (5) HMRPGV i = n / ∑ j = 1 n 1 / Vg ij where in: n is the number of evaluation environments of the genotype i ; Vgij is the genotypic value of the genotype i in the environment j , expressed as a proportion of the average of this environment. The HMRPGV method (RESENDE, 2007a) has the advantages of providing adaptability and genotypic-rather than phenotypic-stability; allowing the managing of heterogeneity of variances; and providing already-penalized genetic values of instability. Moreover, this method can be applied to any number of environments, eliminate noises of the genotype × environment interaction (G×E), while considering the heritability of these effects; and allows the computation of genetic gain with selection by the three attributes simultaneously. The analysis of variance was performed using the SAS software (SAS INSTITUTE, 2002). The analyses of adaptability and genotypic stability were performed by the model 54 of the Selegen-Reml/Blup software (RESENDE, 2007b). RESULTS AND DISCUSSION The joint analysis of variance for grain yield is presented in Table 3. Although all genotypes have shown a high inbreeding level and some parentage (Table 1) and underwent several selection cycles, they differed significantly (. <0.0001), denoting the existence of selectable variability. Table 3 Joint analysis of variance for grain yield of 20 semi-erect cowpea genotypes evaluated in 37 environments of the Northeast region of Brazil, from 2013 to 2015 Source of variation DF Mean square p>F Blocks/Environments 111 302367.20 <.0001 Genotypes (G) 36 1319851.80 <.0001 Environments (E) 19 20318301.80 <.0001 G×E 684 199350.10 <.0001 Residue 2109 69965.00 CV (%) 19.71 Overall mean (kg ha-1) 1,342 Barros et al. (2013), Santos et al. (2015) and Barroso et al. (2016), evaluated 20 cowpea genotypes for yield adaptability and stability in the Mid-North region and in Mato Grosso do Sul, Brazil, respectively, and also found differences between homozygous genotypes. The environments also differed for grain yield (. <0.0001), denoting that their eco-physiographic differences (Table 2) combined with the climatic variations affected this trait. Barroso et al. (2016) evaluated the grain yield of 20 cowpea genotypes in six environments of Mato Grosso do Sul and also found differences between the testing environments; affirming that the edaphoclimatic factors had the greatest effect on the adaptability and stability of the genotypes. Ddamulira et al. (2015), who evaluated the yield adaptability and stability of 25 cowpea genotypes in three environments in Uganda, and Oliveira, Fontes and Rocha (2015), who evaluated cowpea genotypes in several environments in the state of Amazonas in Brazil, also found different grain yields depending on the environment. The overall mean grain yield was 1,342 kg ha-1. This mean is well above the national (369 kg ha-1) and world (461.30 kg ha-1) mean for yield of cowpea (FREIRE FILHO, 2011) and is also above the means obtained in other studies on cowpea genotypes in Brazil (BARROS et al., 2013; BARROSO et al., 2016; SANTOS et al., 2015; TORRES et al., 2015, 2016). This shows the great potential of this line group in regard to grain yield and the possibility of selecting lines with higher means than the commercial cultivars. The environment São Raimundo das Mangabeiras-MA 2013 (SRMA13) was the most favorable for grain yield (2,485 kgha-1), while Serra Talhada-PE 2015 (SERT15) was the least favorable (277 kg ha-1) (Figure 1). Figure 1 Behavior of the grain yield of 37 testing environments obtained from evaluation of 20 semi-erect cowpea genotypes in the Northeast region of Brazil from 2013 to 2015 The G×E for grain yield was highly significant (. <0.0001) (Table 3); it showed the different behavior of the genotypes depending on the testing environments. This lead to difficulties in the selection of superior genotypes and recommending cultivars (CARVALHO et al., 2016; ROCHA et al., 2012), since the cultivars adapted to a particular condition may not perform well in other environmental conditions (OLAYIWOLA, SOREMI; OKELEYE, 2015; TEODORO et al., 2015). According to estimates of components of variance (REML), the environmental variance contributed most to the phenotypic variance (Table 4). It represented 64% of this variance, followed by the G×E (30%), and genotypic (6%) variances. Table 4 Estimates of components of variance (individual REML) and genetic parameters obtained from the evaluation of 20 semi-erect cowpea genotypes in 37 environments in the Northeast region of Brazil, from 2013 to 2015 Parameter Estimate Genotypic variance (σ2g) 7162.05 Residual variance (σ2a) 70027.28 Genotype × environment interaction variance (σ2ga) 32780.83 Phenotypic variance (σ2f) 109970.17 Heritability genotype mean (h2a) 0.84 Genotype selection Accuracy (Acgen) 0.92 Genotypic correlation between environments (rgloc) 0.18 Relative coefficient of variation (Cvg%/Cve%) 0.32 The preponderance of the environmental variance in relation to the other components of the phenotypic variance are in agreement with the results found in the analysis of variance (Table 3), the information of the test environments (Table 2), and the environment means (Figure 1), which showed marked contrasts between testing environments for grain yield. Studies evaluating genotypes of common bean (CHIORATO et al., 2008; PEREIRA et al., 2016) and cowpea (TORRES et al., 2015; OLIVEIRA; FONTES; ROCHA, 2015) in multi-environments also found a higher proportion of environmental variance than genotypic and environmental variances for grain yield. The low genotype variance found in the group of genotypes evaluated (Table 4) was expected considering that some lines showed relation to each other (Table 1) and all are highly inbred, i.e., they have already undergone several selection cycles for high grain yield. Torres et al. (2015, 2016) evaluated the grain yield of 20 cowpea genotypes in multi-environments of Mato Grosso do Sul and also found lower estimates for the genotypic variance than environmental and G×E variances. The trait yield is controlled by several genes and, therefore, is heavily affected by the environment. In spite of the great effect of the environmental factors, denoted by the relative coefficient of variation (0.32), the heritability at an average genotypic level among the various environments was high (0.84) since the environmental effects were minimized. This allowed a high accuracy (0.92) in the selection of lines based on the average of the environments (Table 4). The heritability obtained in the present study was higher than those estimated by Torres et al. (2015, 2016), who evaluated the grain yield of 20 cowpea genotypes in multi-environment of Mato Grosso do Sul and found estimates of 0.68 and 0.54. According to Chiorato et al. (2008), heritability at average level is determined based on the number of replicates and evaluated plants. In the case of the present study, the size of the evaluation area in the experimental unit may have contributed positively to minimize the environmental effects, since the genotypes were represented by 80 plants in the evaluation area of the plot. The G×E variance was the second most important (30%) (Table 4), which resulted from the low genotypic correlation between environments (0.18). These results showed the existence of G×E of complex-type, and that the best lines in one environment will not necessarily be the best in others (RESENDE, 2007a). This represents a certain difficulty in the selection of genotypes with wider adaptation, which justifies considering in the selection of the lines, their stabilities and adaptabilities. Barros et al. (2013) evaluated the grain yield of 20 cowpea genotypes in the Mid-North region of Brazil and also found the G×E as the second factor affecting the phenotypic variance. On the other hand, Torres et al. (2016) observed similar percentages for the G×E and environmental variances. According to the mean component estimates (BLUP) and the confidence intervals associated with genotypic values (u+g), the lines G31 (MNC04-795F-153), G34 (MNC04-795F-159), and G26 (MNC04-769F-62) were superior than most of the genotypes evaluated and presented the highest genetic gains compared to the overall mean, with 12.45%; 11.22%; and 9.51%, respectively (Table 5). These gains were lower than those observed by Torres et al. (2016), who evaluated the grain yield of 20 cowpea genotypes in four environments of Mato Grosso do Sul and found gain estimates for the two best genotypes of 18.79% and 18.04%. Table 5 Estimates of mean components (individual BLUP) of the effects (g) and predicted genotypic values (u+g) free from interaction with environments, confidence interval lower limit (CILL), confidence interval higher limit (CIHL) and genetic gain (Gg) of 20 semi-erect cowpea genotypes evaluated in 37 environments of the Northeast region of Brazil, from 2013 to 2015 Order G u+g (CILL - CIHL)1 Gg G31 167.03 1,508.84 1,434.38 - 1,583.30 167.03 G34 134.17 1,476.85 1,401.52 - 1,550.45 150.60 G26 81.78 1,423.59 1,349.13 - 1,498.05 127.66 G36 40.59 1,382.41 1,307.95 - 1,456.87 105.90 G33 38.27 1,380.08 1,305.62 - 1,454.54 92.37 G32 37.47 1,379.28 1,304.81 - 1,453.74 83.22 G28 36.81 1,378.17 1,304.15 - 1,453.08 76.59 G25 19.26 1,361.07 1,286.61 - 1,435.53 69.42 G40 15.37 1,357.18 1,282.71 - 1,431.64 63.42 G37 -2.93 1,338.88 1,264.41 - 1,413.34 56.78 G39 -10.35 1,331.46 1,256.99 - 1,405.92 50.68 G30 -15.60 1,326.20 1,251.74 - 1,400.67 45.15 G35 -24.26 1,317.55 1,243.09 - 1,392.01 39.81 G24 -24.66 1,317.15 1,242.69 - 1,391.61 35.21 G29 -27.61 1,314.20 1,239.74 - 1,388.66 31.02 G22 -53.99 1,287.82 1,213.35 - 1,362.28 25.71 G27 -63.47 1,278.34 1,203.88 - 1,352.81 20.46 G38 -70.56 1,271.24 1,196.78 - 1,345.71 15.41 G21 -102.49 1,239.32 1,164.86 - 1,313.78 9.20 G23 -174.82 1,166.99 1,092.53 - 1,241.46 0 Overall mean (u) 1,341.81 1Confidence interval associated to the genotypic value estimates, at 95% probability The genetic gain depends on the differential of the selection and heritability of the trait. Although the heritability of grain yield was high, a factor that may have led to lower gains than those observed by Torres et al. (2015, 2016) was the differences between the overall mean and the means of the superior genotypes, which in the present study were small, relatively to the selection differentials founded by the authors above. The results of stability (HMGV), adaptability (RPGV), and simultaneous stability and adaptability (HMRPGV) of the genotypes evaluated are presented in Table 6. The five best genotypes, based on the criteria HMGV, RPGV, and HMRPGV were not necessarily the best based on the criterion of the mean genotypic value (Table 5). The coincidence was 80% among the five best genotypes and there was an inversion of order among the coincident ones between HMGV and RPGV. According to Resende (2007a), the use of these attributes or selection criteria can provide further refinement in selection. Table 6 Genetic value stability (HMGV), genetic values adaptability (RPGV), simultaneous genetic value adaptability and stability (HMRPGV), genotypic value capitalizing the adaptability (RPGV*OM) and genotypic value penalized by instability and capitalized by adaptability (HMRPGV*OM) of 20 semi-erect cowpea genotypes evaluated in 37 environments of Northeast region of Brazil, from 2013 to 2015 Order HMGV Order RPGV RPGV*OM Order HMRPGV HMRPGV*OM G34 1,229 G31 1.1392 1,528 G31 1.1272 1,512 G31 1,229 G34 1.1196 1,502 G34 1.1074 1,485 G36 1,175 G26 1.0734 1,440 G26 1.0578 1,419 G26 1,129 G36 1.0489 1,407 G36 1.0367 1,391 G25 1,127 G28 1.0315 1,384 G28 1.0223 1,371 G28 1,117 G25 1.0304 1,382 G32 1.0181 1,366 G37 1,117 G33 1.0296 1,381 G33 1.0162 1,363 G32 1,106 G32 1.0291 1,380 G25 1.0111 1,356 G33 1,081 G37 1.0060 1,350 G37 0.9947 1,334 G40 1,078 G40 1.0050 1,348 G40 0.9933 1,332 G22 1,070 G29 0.9882 1,326 G30 0.9719 1,304 G30 1,068 G30 0.9848 1,321 G29 0.9707 1,302 G35 1,066 G35 0.9844 1,321 G24 0.9573 1,284 G29 1,044 G39 0.9828 1,319 G22 0.9516 1,276 G39 1,041 G24 0.9674 1,298 G35 0.9503 1,275 G24 1,014 G22 0.9648 1,294 G39 0.9463 1,269 G27 1,004 G27 0.9446 1,267 G27 0.9326 1,251 G38 992 G38 0.9359 1,256 G38 0.9042 1,213 G21 918 G21 0.8953 1,201 G21 0.8811 1,182 G23 818 G23 0.8390 1,126 G23 0.8086 1,085 Overall mean 1,342 Similar results were obtained by Torres et al. (2016), who evaluated the yield of 20 cowpea genotypes in the state of Mato Grosso do Sul, and also observed the same percentage of coincidence between the ordering provided by HMGV and RPGV. The three best lines (G31 - MNC04-795F-153, G34 - MNC04-795F-159 and G26 - MNC04-769F-62) by the criterion HMRPGV*OM, had grain yield of 1,512 kg ha-1, 1,485 kg ha-1 and 1,419 kg ha-1 (Table 6), i.e., an average superiority of 13%, 11%, and 10%, respectively, over the overall mean of the 37 environments. According to Resende (2007a), these values are obtained through a process that already penalizes the lines for the instability in the environments and capitalizes the capacity of response (adaptability) to the improvement of the environment. These properties are intrinsic to the HMRPGV method. The values of RPGV and HMRPGV (Table 6) indicate the average superiority of the genotype in relation to the average of a given environment. Thus, the line MNC04-795F-153 (G31) responds on average 1.1272 times the mean of any environment in which it is grown. The four best genotypes in genotypic value (Table 5) were also the best in HMRPGV. Similar results were found by Torres et al. (2016), who evaluated the yield adaptability and stability of 20 cowpea genotypes in several environments in the State of Mato Grosso do Sul. Based on these authors, this is an indication that the genotypes evaluated in the present work had high adaptive synergism in the 37 environments tested and good predictability, i.e., maintenance of yield in the environments. In general, the lines MNC04-795F-159 (G31) and MNC04-795F-153 (G34) were superior for yield, adaptability, and stability, and can be recommended for the evaluated environments in the Northeast region of Brazil, with a lower risk of losses in grain yield due to unpredictable environmental factors. According to Torres et al. (2016), genotypes that simultaneously have these three attributes can be used as selection criteria in breeding programs. The lines G31 and G34 are sisters, presenting the same genealogy, and belong to the same commercial subclass-mulatto (Table 1). Their grain yield is also similar (Table 6). Thus, regarding the releasing of cultivars, the line G31 should be chosen, since this line better combines the attributes of yield, adaptability, and stability. CONCLUSIONS The line MNC04-795F-159 presents greater predictability of grain yield in the environmental conditions evaluated in the Northeast region of Brazil; The line MNC04-795F-153 combines high yield, adaptability and genotypic stability, and can be recommended and grown with greater probability of success in the edaphoclimatic conditions evaluated in the Northeast region of Brazil. 1 Pesquisa desenvolvida pela Embrapa (Empresa Brasileira de Pesquisa Agropecuária), Programa de Melhoramento de Feijão-caupi da Embrapa Meio-Norte, região Nordeste, Teresina-PI, Brasil ACKNOWLEDGEMENTS The authors thank the partners of the genetic breeding network for cowpea in the Northeast Region for the structural support and personnel in conducting the tests; and Dr. Marcos Deon Vilela de Resende, for the assistance in the statistical analysis of adaptability and stability via REML/BLUP procedure. REFERENCES BARROS, M. A. et al. 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